Large 3D engineering models like architectural designs, chemical plants and mechanical CAD (computer-aided design) designs are increasingly being deployed in various virtual world applications, such as Second Life™ and Google Earth™ In most engineering models there are a large number of small to medium sized connected components, each having up to a few hundred polygons on average. Moreover, this type of models has a number of geometric features that is repeated in various positions, scales and orientations, such as the “Meeting room” shown in FIG. 1.
Various algorithms have been proposed to compress 3D meshes efficiently since the early 1990s. Most of the existing 3D mesh compression algorithms work best for smooth surfaces with dense meshes of small triangles. However, large 3D models of the engineering class usually have a large number of connected components, with small numbers of large triangles, often with arbitrary connectivity. The architectural and mechanical CAD models typically have many non-smooth surfaces making these methods less suitable. Moreover, most of the earlier efforts deal with each connected component separately. In fact, the encoder performance can be greatly increased by removing the redundancy in the representation of repeating geometric feature patterns. To enable compact storage and fast transmission of large 3D engineering models, an efficient compression strategy specially designed for 3D mesh models (e.g. of engineering models) is needed. A good compression method for large 3D engineering models should be able to automatically discover the repeating geometry feature patterns and to effectively encode the necessary information for reconstructing the original model from the discovered geometry feature patterns.
[SBM01]1 proposed a method for automatically discovering repeating geometric features in large 3D engineering models. However, [SBM01] does not provide a complete compression scheme for 3D mesh models, particularly engineering models. Further, [SBM01] uses PCA (Principal Component Analysis) of positions of vertices of a component. As a consequence, components with same geometry and different connectivity will have same mean and same orientation axes. Further, [SBM01] is not suitable for detecting repeating patterns in various scales. Two components that differ only in scale (i.e. size) will not be recognized as repeating features of the same equivalence class. Further, it is desirable to compress the encoded data even more than described in [SBM01]. 1[SBM01]: D. Shikhare, S. Bhakar and S. P. Mudur. “Compression of Large 3D Engineering Models using Automatic Discovery of Repeating Geometric Features”, 6th International Fall Workshop on Vision, Modeling and Visualization (VMV2001), Nov. 21-23, 2001, Stuttgart, Germany
Thus, the method of [SBM01] needs further improvement.